L2M-GAN: Learning To Manipulate Latent Space Semantics for Facial Attribute Editing
Guoxing Yang, Nanyi Fei, Mingyu Ding, Guangzhen Liu, Zhiwu Lu, Tao Xiang
2021Year
6Top-tier citations
Abstract
Source Smiling Gender Age Bangs Eyeglasses Figure 1. Attribute editing results by our L2M-GAN on CelebA-HQ. The first column shows the real source images, and each of the other columns shows the results of editing a specific attribute. Each edited image has an attribute value opposite to that of the source one.
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Cited by top-tier papers6
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- Reference-Based 3D-Aware Image Editing with TriplanesBahri Batuhan Bilecen, Yigit Yalin, Ning Yu, Aysegul DundarCVPR 2025
- StyleRes: Transforming the Residuals for Real Image Editing with StyleGANHamza Pehlivan, Yusuf Dalva, Aysegul DundarCVPR 2023
Builds on9
- Few-Shot Unsupervised Image-to-Image TranslationMing-Yu Liu, Xun Huang, Arun Mallya, Tero Karras et al.ICCV 2019 · 668 citations
- On the "steerability" of generative adversarial networksAli Jahanian, Lucy Chai, Phillip IsolaICLR 2020 · 421 citations
- RankSRGAN: Generative Adversarial Networks With Ranker for Image Super-ResolutionWenlong Zhang, Yihao Liu, Chao Dong, Yu QiaoICCV 2019 · 406 citations
- GANalyze: Toward Visual Definitions of Cognitive Image PropertiesLore Goetschalckx, Alex Andonian, Aude Oliva, Phillip IsolaICCV 2019 · 345 citations
- Latent Space Factorisation and Manipulation via Matrix Subspace ProjectionXiao Li, Chenghua Lin, Ruizhe Li, Chaozheng Wang et al.ICML 2020 · 30 citations
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